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Статті в журналах з теми "QBF solver"
Weihua, Su, Yin Minghao, Wang Jianan, and Zhou Junping. "Message Passing Algorithm for Solving QBF Using More Reasoning." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/165927.
Повний текст джерелаLonsing, Florian, and Armin Biere. "DepQBF: A Dependency-Aware QBF Solver." Journal on Satisfiability, Boolean Modeling and Computation 7, no. 2-3 (August 1, 2010): 71–76. http://dx.doi.org/10.3233/sat190077.
Повний текст джерелаChen, Pei-Wei, Yu-Ching Huang, and Jie-Hong R. Jiang. "A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 3697–706. http://dx.doi.org/10.1609/aaai.v35i5.16486.
Повний текст джерелаGoultiaeva, Alexandra, and Fahiem Bacchus. "Exploiting QBF Duality on a Circuit Representation." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 71–76. http://dx.doi.org/10.1609/aaai.v24i1.7548.
Повний текст джерелаSchuppan, Viktor. "Enhanced Unsatisfiable Cores for QBF: Weakening Universal to Existential Quantifiers." International Journal on Artificial Intelligence Tools 29, no. 03n04 (June 2020): 2060012. http://dx.doi.org/10.1142/s021821302060012x.
Повний текст джерелаGiunchiglia, E., M. Narizzano, and A. Tacchella. "Clause/Term Resolution and Learning in the Evaluation of Quantified Boolean Formulas." Journal of Artificial Intelligence Research 26 (August 17, 2006): 371–416. http://dx.doi.org/10.1613/jair.1959.
Повний текст джерелаNarizzano, Massimo, Luca Pulina, and Armando Tacchella. "Report of the Third QBF Solvers Evaluation1." Journal on Satisfiability, Boolean Modeling and Computation 2, no. 1-4 (March 1, 2006): 145–64. http://dx.doi.org/10.3233/sat190019.
Повний текст джерелаTentrup, Leander. "CAQE and QuAbS: Abstraction Based QBF Solvers." Journal on Satisfiability, Boolean Modeling and Computation 11, no. 1 (September 1, 2019): 155–210. http://dx.doi.org/10.3233/sat190121.
Повний текст джерелаLonsing, Florian, and Armin Biere. "Efficiently Representing Existential Dependency Sets for Expansion-based QBF Solvers." Electronic Notes in Theoretical Computer Science 251 (September 2009): 83–95. http://dx.doi.org/10.1016/j.entcs.2009.08.029.
Повний текст джерелаPeitl, Tomáš, Friedrich Slivovsky, and Stefan Szeider. "Dependency Learning for QBF." Journal of Artificial Intelligence Research 65 (June 18, 2019): 181–208. http://dx.doi.org/10.1613/jair.1.11529.
Повний текст джерелаДисертації з теми "QBF solver"
Fernandez, Davila Jorge Luis. "Planification cognitive basée sur la logique : de la théorie à l'implémentation." Electronic Thesis or Diss., Toulouse 3, 2022. http://thesesups.ups-tlse.fr/5491/.
Повний текст джерелаIn this thesis, we introduced a cognitive planning framework that can be used to endow artificial agents with the necessary skills to represent and reason about other agents' mental states. Our cognitive planning framework is based on an NP-fragment of an epistemic logic with a semantics exploiting belief bases and whose satisfiability problem can be reduced to SAT. We detail the set of translations for the reduction of our fragment to SAT. In addition, we provide complexity results for checking satisfiability of formulas in our NP-fragment. We define a general architecture for the cognitive planning problem. Afterward, we define two types of planning problem: informative and interrogative, and we find the complexity of finding a solution for the cognitive planning problem in both cases. Furthermore, we illustrated the potential of our framework for applications in human-machine interaction with the help of two examples in which an artificial agent is expected to interact with a human agent through dialogue and to persuade the human to behave in a certain way. Moreover, we introduced a formalization of simple cognitive planning as a quantified boolean formula (QBF) with an optimal number of quantifiers in the prefix. The model for cognitive planning was implemented. We describe how to represent and generate the belief base. Furthermore, we demonstrate how the machine performs the reasoning process to find a sequence of speech acts intended to induce a potential intention in the human agent. The implemented system has three main components: belief revision, cognitive planning, and the translator module. These modules work integrated to capture the human agent's beliefs during the human-machine interaction process and generate a sequence of speech acts to achieve a persuasive goal. Finally, we present an epistemic language to represent the beliefs and actions of an artificial player in the context of the board game Yokai. The cooperative game Yokai requires a combination of theory of mind (ToM), temporal and spatial reasoning for an artificial agent to play effectively. We show that the language properly accounts for these three dimensions and that its satisfiability problem is NP-complete. We implement the game and perform experiments to compare the cooperation level between agents when they try to achieve a common goal by analyzing two scenarios: when the game is played between a human and the artificial agent versus when two humans play the game
Goultiaeva, Alexandra. "Exploiting Problem Structure in QBF Solving." Thesis, 2014. http://hdl.handle.net/1807/44111.
Повний текст джерелаЧастини книг з теми "QBF solver"
Giunchiglia, Enrico, Massimo Narizzano, and Armando Tacchella. "QuBE++: An Efficient QBF Solver." In Formal Methods in Computer-Aided Design, 201–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30494-4_15.
Повний текст джерелаOlivo, Oswaldo, and E. Allen Emerson. "A More Efficient BDD-Based QBF Solver." In Principles and Practice of Constraint Programming – CP 2011, 675–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23786-7_51.
Повний текст джерелаGoultiaeva, Alexandra, Vicki Iverson, and Fahiem Bacchus. "Beyond CNF: A Circuit-Based QBF Solver." In Lecture Notes in Computer Science, 412–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02777-2_38.
Повний текст джерелаBryant, Randal E., and Marijn J. H. Heule. "Dual Proof Generation for Quantified Boolean Formulas with a BDD-based Solver." In Automated Deduction – CADE 28, 433–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79876-5_25.
Повний текст джерелаBalyo, Tomáš, and Florian Lonsing. "HordeQBF: A Modular and Massively Parallel QBF Solver." In Theory and Applications of Satisfiability Testing – SAT 2016, 531–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40970-2_33.
Повний текст джерелаHeyman, Tamir, Dan Smith, Yogesh Mahajan, Lance Leong, and Husam Abu-Haimed. "Dominant Controllability Check Using QBF-Solver and Netlist Optimizer." In Lecture Notes in Computer Science, 227–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09284-3_18.
Повний текст джерелаLonsing, Florian, and Uwe Egly. "DepQBF 6.0: A Search-Based QBF Solver Beyond Traditional QCDCL." In Automated Deduction – CADE 26, 371–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63046-5_23.
Повний текст джерелаKlieber, William, Samir Sapra, Sicun Gao, and Edmund Clarke. "A Non-prenex, Non-clausal QBF Solver with Game-State Learning." In Theory and Applications of Satisfiability Testing – SAT 2010, 128–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14186-7_12.
Повний текст джерелаHeisinger, Maximilian, Martina Seidl, and Armin Biere. "ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving." In Tools and Algorithms for the Construction and Analysis of Systems, 426–47. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30823-9_22.
Повний текст джерелаLonsing, Florian, and Uwe Egly. "Evaluating QBF Solvers: Quantifier Alternations Matter." In Lecture Notes in Computer Science, 276–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98334-9_19.
Повний текст джерелаТези доповідей конференцій з теми "QBF solver"
Rabe, Markus N., and Leander Tentrup. "CAQE: A Certifying QBF Solver." In 2015 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2015. http://dx.doi.org/10.1109/fmcad.2015.7542263.
Повний текст джерелаPigorsch, Florian, and Christoph Scholl. "Exploiting structure in an AIG based QBF solver." In 2009 Design, Automation & Test in Europe Conference & Exhibition (DATE'09). IEEE, 2009. http://dx.doi.org/10.1109/date.2009.5090919.
Повний текст джерелаSantos, Rafael, Joao Afonso, and Jose Monteiro. "Short-circuit Analysis using a Parallel QBF Solver." In 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS). IEEE, 2020. http://dx.doi.org/10.1109/dcis51330.2020.9268636.
Повний текст джерелаPigorsch, Florian, and Christoph Scholl. "An AIG-Based QBF-solver using SAT for preprocessing." In the 47th Design Automation Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1837274.1837318.
Повний текст джерелаYu, Yinlei, and Sharad Malik. "Validating the result of a Quantified Boolean Formula (QBF) solver." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1120725.1120821.
Повний текст джерелаMarin, Paolo, Massimo Narizzano, Enrico Giunchiglia, Matthew Lewis, Tobias Schubert, and Bernd Becker. "Comparison of knowledge sharing strategies in a parallel QBF solver." In Simulation (HPCS). IEEE, 2009. http://dx.doi.org/10.1109/hpcsim.2009.5195312.
Повний текст джерелаLagniez, Jean-Marie, Daniel Le Berre, Tiago de Lima, and Valentin Montmirail. "A Recursive Shortcut for CEGAR: Application To The Modal Logic K Satisfiability Problem." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/94.
Повний текст джерелаBöhm, Benjamin, Tomáš Peitl, and Olaf Beyersdorff. "QCDCL with Cube Learning or Pure Literal Elimination - What is Best?" In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/248.
Повний текст джерелаLee, Nian-Ze, Yen-Shi Wang, and Jie-Hong R. Jiang. "Solving Exist-Random Quantified Stochastic Boolean Satisfiability via Clause Selection." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/186.
Повний текст джерелаFernandez, Jorge, Olivier Gasquet, Andreas Herzig, Dominique Longin, Emiliano Lorini, Frédéric Maris, and Pierre Régnier. "TouIST: a Friendly Language for Propositional Logic and More." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/756.
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